롱테일 상품의 일대일 마케팅 활성화를 위한 추천시스템에 관한 연구: 고객의 영화 선호도 예측에의 적용
Recommeder Systems for 1-to-1 Marketing of Tail Products: Application to Movie Rating Prediction
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초록
인터넷기업의 증대에 따라서 오프라인의 제한된 진열공간의 한계를 넘어서 다양한 상품과 서비스가 유통되게 되었고, 고객은 적극적으로 자신의 요구사항을 표현하게 되었다. 이러한 경제구도의 변화로 인하여, 과거 히트상품의 영향력은 줄어들고 다수의 비인기 상품이 수익을 창출하는 비중은 점차 증대되고 있다. 이에 따라서 e-biz기업은 고객의 성향 및 선호도를 고려한 개인화 추천서비스를 통하여 일대일 마케팅을 수행하고 있으나, 기존 대부분의 추천기법은 비인기 상품, 이른바 테일 상품에 대한 추천 성능 저하 문제를 고려하고 있지 못하다. 본 연구는 추천기법 중, 가장 널리 활용되고 있는 협업필터링의 비인기 상품에 대한 성능 저하문제를 제기하고, 이를 클러스터링과 회귀분석을 결합하여 해결하는 새로운 하이브리드 상품추천방안을 제안한다. 제안된 모형을 MovieLens데이타셋을 활용하여 고객의 영화 선호도 예측에 적용한 결과, 협업필터링을 사용한 것보다 비인기 상품에 대한 고객 선호도를 더욱 정확하게 예측할 수 있음을 확인하였다.
Abstract
The paper studies the long tail recommendation problem for the item-based collaborative filtering method and suggest the new recommendation method for the tail products. In internet era, the power of the hit items are decreased, on the other hand, the power of the tail items are increased. Thus, recommending the right products in the tail part as well as the head become very important. Recommender Systems have been used as successful 1-to-1 marketing tools, however they often ignore the unpopular items having only few rating. In this research we suggest the new recommendation technique works well both the head and the tail. The suggested method splits the whole products into the head and the tail parts and applies item-to-item collaborative filtering method in the head parts, however apply the new hybrid dataming technique using clustering and regression in the tail part. We apply this method to the real movie dataset. The result shows that the suggested method reduces the recommendation error rates for the tail items, while maintaining reasonable computational performance.
참고문헌 (25)
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